Page 78 - Computational Retinal Image Analysis

References 69

retinal vessel segmentation via locally adaptive derivative frames in orientation scores,
IEEE Trans. Med. Imaging 35 (12) (2016) 2631–2644.
[13] J. Sivaswamy, A. Agarwal, M. Chawla, A. Rani, T. Das, Extraction of capillary non-
perfusion from fundus fluorescein angiogram, A. Fred, J. Filipe, H. Gamboa (Eds.),
Biomedical Engineering Systems and Technologies, Springer, Berlin, Heidelberg, 2009,
pp. 176–188.
[14] Y. Zhao, Y. Liu, X. Wu, S. Harding, Y. Zheng, Retinal vessel segmentation: an efficient
graph cut approach with Retinex and local phase, PLoS ONE 10 (4) (2015) 1–22.
[15] K. Zuiderveld, Contrast limited adaptive histogram equalization, Graphics Gems IV,
Academic Press, San Diego, CA, 1994, pp. 474–485.
[16] G. Manikis, V. Sakkalis, X. Zabulis, P. Karamaounas, A. Triantafyllou, S. Douma,
C. Zamboulis, K. Marias, An image analysis framework for the early assessment of
hypertensive retinopathy signs, IEEE E-Health and Bioengineering Conference, 2011,
pp. 1–6.
[17] U. Acharya, E. Ng, J. Tan, V. Sree, K. Ng, An integrated index for the identification
of diabetic retinopathy stages using texture parameters, J. Med. Syst. 36 (3) (2012)
2011–2020.
[18] M. Zhou, K. Jin, S. Wang, J. Ye, D. Qian, Color retinal image enhancement based on
luminosity and contrast adjustment, IEEE Trans. Biomed. Eng. 65 (3) (2018) 521–527.
[19] Sonali, S. Sahu, A. Singh, S. Ghrera, M. Elhoseny, An approach for de-noising and
contrast enhancement of retinal fundus image using CLAHE, Opt. Laser Technol. 110
(2019) 87–98.
[20] A. Ajaz, B. Aliahmad, D. Kumar, A novel method for segmentation of infrared scanning
laser ophthalmoscope (IR-SLO) images of retina, IEEE Engineering in Medicine and
Biology Society, 2017, pp. 356–359.
[21] M. Esmaeili, H. Rabbani, A.M. Dehnavi, Automatic optic disk boundary extraction
by the use of curvelet transform and deformable variational level set model, Pattern
Recogn. 45 (7) (2012) 2832–2842, https://doi.org/10.1016/j.patcog.2012.01.002.
[22] A. Sopharak, B. Uyyanonvara, S. Barman, T. Williamson, Automatic detection of
diabetic retinopathy exudates from non-dilated retinal images using mathematical
morphology methods, Comput. Med. Imaging Graph. 32 (2008) 720–727.
[23] S.M. Shankaranarayana, K. Ram, A. Vinekar, K. Mitra, M. Sivaprakasam, Restoration
of neonatal retinal images, Proceedings of the Ophthalmic Medical Image Analysis
Third International Workshop, OMIA, 2016, pp. 49–56.
[24] A. Aibinu, M. Iqbal, M. Nilsson, M. Salami, A new method of correcting uneven
illumination problem in fundus images, International Conference on Robotics, Vision,
Information, and Signal Processing, 2007, pp. 445–449.
[25] K. Huang, M. Yan, A local adaptive algorithm for microaneurysms detection in digital
fundus images, Computer Vision for Biomedical Image Applications, 2005, pp.
103–113.
[26] R. GeethaRamani, L. Balasubramanian, Retinal blood vessel segmentation employing
image processing and data mining techniques for computerized retinal image analysis.
Retinal blood vessel segmentation in fundus images, Biocybern. Biomed. Eng. 36 (1)
(2016) 102–118.
[27] M. Foracchia, E. Grisan, A. Ruggeri, Luminosity and contrast normalization in retinal
images, Med. Image Anal. 9 (3) (2005) 179–190.
[28] H. Narasimha-Iyer, A. Can, B. Roysam, V. Stewart, H.L. Tanenbaum, A. Majerovics,
H. Singh, Robust detection and classification of longitudinal changes in color retinal